@inproceedings{soultanaAdaptiveHeterogeneousEnsemble2025,

title = {Adaptive Heterogeneous Ensemble Learning for Attack Detection in IoT Networks},

author = {O. A. Soultana and H. Moudoud},

url = {https://www.scopus.com/pages/publications/105033149093?origin=resultslist},

doi = {10.1109/SMC58881.2025.11343130},

isbn = {1062922X (ISSN); 979-833153358-8 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},

pages = {27–32},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The proliferation of Internet of Things (IoT) devices has introduced significant security vulnerabilities, particularly in detecting zero-day attacks within highly dynamic and heterogeneous environments. Traditional machine learning models often fall short due to their static nature and computational demands. In this paper, we propose an adaptive ensemble learning framework that dynamically selects optimal detection models on a per-attack-class basis to improve detection accuracy while maintaining computational efficiency. Our approach combines multiple base classifiers (Random Forest, K-Nearest Neighbors, and Support Vector Machine) using ensemble techniques including bagging, boosting, and stacking. Ensemble techniques such as Bagging, Boosting, Voting, and Stacking. The key innovation lies in a class-aware model selection mechanism that identifies the most effective classifier-ensemble combination for each specific attack category, rather than applying a single model across all threat types. This targeted approach recognizes that different attack patterns exhibit distinct characteristics that may be better captured by different algorithmic approaches. Finally, we propose a decision-rule mechanism that selects the best-performing model for each attack class to improve detection accuracy. The proposed framework is evaluated through extensive experiments. The results show that our approach significantly enhances classification performance, especially for complex and rare attack types. © 2025 IEEE.},

note = {Journal Abbreviation: Conf. Proc. IEEE Int. Conf. Syst. Man Cybern.},

keywords = {Attack detection, Classification (of information), Computational efficiency, Detection accuracy, Ensemble learning, Ensemble techniques, Heterogeneous ensembles, Internet of thing security, Internet of things, Intrusion Detection, Intrusion-Detection, IoT Security, Learning systems, Nearest neighbor search, Security vulnerabilities, Stackings, Support vector machines, Zero-day attack, Zero-day detection},

pubstate = {published},

tppubtype = {inproceedings}

}